Decision making influences movement variability and performance of high-level female football players in an elastic resistance task.

Introduction: The inclusion of sport-specific constraints in resistance training promotes the development of player abilities in an integrated way, which maximises the effectiveness of player adaptations induced by training. Considering that perceptual-cognitive abilities play a fundamental role in football, decision making could be introduced to enhance the cognitive similarity of resistance tasks to sport actions. However, it is unknown how decision making as a constraint could affect the player during an elastic resistance task. Therefore, the aim of this study was to investigate the effects of decision making of high-level female football players on movement variability and performance during an elastic band resistance task. Methods: Twenty-three high-level female football players performed the elastic resistance task with a ball, both as attackers and as defenders without decision making (NDM) and with decision making (DM). The movement variability was quantified using the sample entropy derived from the acceleration recorded with an accelerometer placed at the lower back of each player. The passing accuracy of the attacker was quantified using a scoring scale. Results: Results revealed that adding decision making to an elastic resistance task increased the movement variability of the defender but did not affect the movement variability of the attacker. In contrast, the passing accuracy of the attacker was reduced. Overall, the attacker had a higher movement variability compared to the defender. Discussion: These findings suggest that decision making, as a football-specific constraint, can enhance the potential of an elastic resistance task in training. This is due to the fact that it reduces control and regularity of movement for the defensive role player and increases technical difficulty for the attacking role player. Furthermore, these effects are beneficial, as they can promote the adaptive processes necessary to optimise the performance of the players.

Adding mechanical vibration to a half squat with different ballasts and rhythms increases movement variability.

The aim of this study was to determine whether whole body vibration increases movement variability while performing a half squat with different ballasts and rhythms through entropy. A total of 12 male athletes (age: 21.24 ± 2.35 years, height: 176.83 ± 5.80 cm, body mass: 70.63 ± 8.58 kg) performed a half squat with weighted vest, dumbbells and bar with weights suspended with elastic bands, with and without vibration at the squat rhythm of 40 and 60 bpm. Each ballast corresponded to 15% of the body mass. The movement variability was analysed by calculating the sample entropy of the acceleration signal, recorded at the waist using an accelerometer. With vibration, differences were found between weighted vest and dumbbells (t(121) = -8.81, p < 0.001 at 40 bpm; t(121) = -8.18, p < 0.001 at 60 bpm) and between weighted vest and bar at both rhythms (t(121) = -8.96, p < 0.001 at 40 bpm; t(121) = -8.83, p < 0.001 at 60 bpm). Furthermore, a higher sample entropy was obtained at 40 bpm with all ballasts (t(121) = 5.65, p < 0.001 with weighted vest; t(121) = 6.27, p < 0.001 with dumbbells; t(121) = 5.78, p < 0.001 with bar). No differences were found without vibration. These findings reveal that adding mechanical vibration to a half squat produces a non-proportional increase in movement variability, being larger when the ballast is placed on the upper limbs and when performed at a slow rhythm.

Influence of Turn Cycle Structure on Performance of Elite Alpine Skiers Assessed through an IMU in Different Slalom Course Settings.

Small differences in turn cycle structure, invisible to the naked eye, could be decisive in improving descent performance. The aim of this study was to assess the influence of turn cycle structure on the performance of elite alpine skiers using an inertial measurement unit (IMU) in different slalom (SL) course settings. Four SL courses were set: a flat-turned (FT), a steep-turned (ST), a flat-straighter (FS) and a steep-straighter (SS). Five elite alpine skiers (21.2 ± 3.3 years, 180.2 ± 5.6 cm, 72.8 ± 6.6 kg) completed several runs at maximum speed for each SL course. A total of 77 runs were obtained. Fast total times correlate with a longer initiation (INI) time in FT, a shorter steering time out of the turn (STEOUT) in the FT and FS and a shorter total steering time (STEIN+OUT) in the FT and SS courses. The linear mixed model used for the analysis revealed that in the FT-course for each second increase in the INI time, the total time is reduced by 0.45 s, and for every one-second increase in the STEOUT and STEIN+OUT times, the total time increases by 0.48 s and 0.31 s, respectively. Thus, to enhance descent performance, the skier should lengthen the INI time and shorten the STEOUT and STEIN+OUT time. Future studies could use an IMU to detect turn phases and analyze them using the other built-in sensors.

Validity of a Magnet-Based Timing System Using the Magnetometer Built into an IMU.

Inertial measurement units (IMUs) represent a technology that is booming in sports right now. The aim of this study was to evaluate the validity of a new application on the use of these wearable sensors, specifically to evaluate a magnet-based timing system (M-BTS) for timing short-duration sports actions using the magnetometer built into an IMU in different sporting contexts. Forty-eight athletes (22.7 ± 3.3 years, 72.2 ± 10.3 kg, 176.9 ± 8.5 cm) and eight skiers (17.4 ± 0.8 years, 176.4 ± 4.9 cm, 67.7 ± 2.0 kg) performed a 60-m linear sprint running test and a ski slalom, respectively. The M-BTS consisted of placing several magnets along the course in both contexts. The magnetometer built into the IMU detected the peak-shaped magnetic field when passing near the magnets at a certain speed. The time between peaks was calculated. The system was validated with photocells. The 95% error intervals for the total times were less than 0.077 s for the running test and 0.050 s for the ski slalom. With the M-BTS, future studies could select and cut the signals belonging to the other sensors that are integrated in the IMU, such as the accelerometer and the gyroscope.